The Fertilizer Industry Gains Actionable Timely Insights with Advanced Analytics – Part 1 of 2
Fertilizer History: From Manure to Agile Future-Proof Industrial Manufactured Products
This blog is part one of a two-part series about how the fertilizer industry can stay on top of its game through the use of advanced analytics. By using this important tool, it can address its production challenges and achieve production optimization to keep manufacturing fertilizer to help grow food for the world’s population.
As the population is ever increasing so will the need for fertilizer. Today’s fertilizer industry is a multibillion-dollar industry and provides hundreds of thousands of jobs around the world. Not many people think about fertilizer unless they are in the industry, farmers, or gardeners so know its importance for improving plant growth and health. And if you aren’t in one of these segments, did you ever happen to wonder how fertilizers came about?
How Fertilizer Use Came About
Researchers reckon that humans started using manure, which was the first fertilizer, about 8000 years ago. One wonders how people so long ago thought to use manure to increase crop yields. Well, they probably simply noticed that there was more plant growth in areas where animals gathered, and they put two & two together about the resulting animal waste dung. These areas of different yield and enhanced productivity would have been obvious to farmers.
Heck, the ancient Egyptians and Babylonians and early Germans and Romans have records of using manure and minerals to improve crop yields, with manure being the principal fertilizer for next millennia.Then in the 18th century, milled bones were recognized as another source of crop fertilizer. Still, by this time not much was known about the actual science of plant nutrition.
This knowledge was the beginning of the modern era of plant nutrition and soil chemistry. Basically, through his research, Liebig concluded that crops exhausted soil nutrients but by adding nitrogen-base fertilizer, optimum crop production could be obtained.
Scientific work continued in this field to further knowledge on plant and soil nutrients along with fertilizer development. The next big advancement occurred with the Haber process, developed by Carl Bosch and Fritz Haber. (Btw, they also won the Nobel prize for this work. And if you’re interested in reading about a value case on the Haber process, have a look at this use case: Waste Reduction on Ammonia Process). This economical and sustainable process uses molecular nitrogen and methane gas to produce ammonia.
Wilhelm Ostwald progressed the fertilizer industry with his Ostwald process which produced chemical fertilizers with nitric acid as the primary raw material. Therefore, by the start of the 20th century, manufacturing processes were developed to produce the basic components of most chemical fertilizers: ammonia and nitric acid. But still at this point, chemical fertilizers had limited use.
Why, you may ask? Well, there were some challenges: because of limited availability, chemical fertilizers were expensive; machined methods for applying fertilizers had not been developed, and more importantly, farmers did not know when or how much fertilizer to apply.
Things progressed, as things will, and another big jump in the industry happened after World War II. As nitrogen is a primary constituent of explosives, its production went out of the roof during WW II. After the war was over, weapons production was replaced by crop production as food stores needed to be replenished. At this time, governments invested heavily in agricultural research which eventually brought about today’s chemical fertilizer industry.
Four Important Challenges the Fertilizer Industry Faces Today
The fertilizer industry is an industrial manufacturing process industry and as such experiences many of the challenges that these industries face. Below are four important challenges the fertilizer industry faces today:
Challenge 1 – Leveraging Production Data in Conjunction with the Knowledge & Skill Set of Process Experts
If we go back a few years ago, large industrial processes faced the challenge of how to store the immense amount of captured sensor data. Nowadays, a lot more is known about historians and databases including cloud storage to take care of the storage problem. But we are still trying to find ways to connect this production data to the knowledge and skill set of the process experts and thus leverage the production data with the craftsmanship of these experts.
Challenge 2 – Missing Substantial Opportunity by Using Overloaded Data Scientists Who Lack Process Knowledge
Obviously, to leverage the captured production time-series data, it must be analyzed, and this is done through data analytics. Usually, industrial process companies use data scientists for this. However, there are problems with this approach as data scientists are overloaded with analytics projects, and while very efficient at writing algorithms and code, they often lack the process knowledge to understand the story the data is telling. They do not have the process knowledge that process experts have resulting in a situation that does not use the knowledge and skill set of the process experts to the highest potential. This results in a substantial missed opportunity to solve process problems and optimize production.
Challenge 3 – Experiencing Time Delays for Analytics Results
The next challenge is the time factor. When you are in need of data analyses to solve production issues, you don’t want to wait for a few days, weeks, and even sometimes months until the data is analyzed to get the results. Rather, you want the analyses to be urgent to get timely actionable answers in the least amount of time as possible. You want to know what is happening, so you can know what corrective actions to take. Time is a lot of money in industrial processes and so saving time means saving money.
Challenge 4 – Tapping into Improvement Potential
The last challenge is tapping into improvement potential of industrial manufacturing processes. Companies want to continuously focus on improving processes and operations for improved KPIs. And this can come about through leveraging the data, by analyzing the data, which can also show improvements to the process experts who would not find these without analyzing the data. These companies already have the data, so it makes darn good sense to use it for the optimum improvement potential.
How the Industry Usually Tackles These Challenges
If you are part of the industrial fertilizer manufacturing industry, you are well aware of these common challenges. So how do you usually go about tackling these? There are three common approaches:
First, you would rely on your existing tools. You would get the data from the historian or from a DCS system, input it to Excel files, and try to do the data analysis from there. But those of us who have used this method know that Excel is not built for handling such large volumes of data or for complicated analyses. (For a more in-depth explanation about Excel’s limitations, have a look at our blog: “To Excel” or “Not to Excel” – That Is the Process Engineering Question).
Second, you could turn to your data scientists. But as mentioned before, data scientists are often overbooked with many analytics projects and are often not able to understand the data, to see the story the data is presenting, because they lack the process knowledge to do so. This approach is time-consuming and costly as the data scientists have to confer with the process experts to understand the data. It’s lost potential and highly inefficient.
Third, you might hesitate to begin data analytics due to the existing data infrastructure. Many times, companies feel that they do not have a 100% complete data infrastructure and thus do not have 100 % complete data. This leads them to believe that they should first focus on having the necessary structure in place before they start analyzing the data. However, this is not the case. With the data and the infrastructure you already have, you can start performing some of the analyses that you are interested in. And you can do this with advanced analytics.
Everyone on Your Process Team Can Do the Analytics Themselves
Look, it’s 2021, and Industry 4.0 and digitalization are in full swing. The fertilizer industry is awakening to the technologies that this era is bringing. One such technology is advanced analytics. You hear “analytics” a lot these days which can lead to “analytics” overload and misunderstanding. What you should know is that this technology is a software that is based on using time-series data that industrial manufacturing processes capture, so operational specialists can analyze, monitor, and predict production performance within its operational context. The key here is that “operational specialists”, who have the essential operational and production knowledge, and not data scientists, can use this software tool.
Advanced analytics democratizes industrial analytics thus empowering all process experts who can and who want to work with the data. This means that everyone, not just the data scientists, but the process, production and maintenance engineers, the production and plant managers, and the operators can do the data analytics themselves. As such, advanced analytics is extremely important and valuable for the fertilizer industry.
Stay tuned for the second part of this series:
The Fertilizer Industry Gains Actionable Timely Insights with Advanced Analytics – Part 2.
You’ll understand clearly how the fertilizer industry can use advanced analytics as we’ll cover an explanation of a generic workflow of solving an industry use case and an explanation of an actual use case of fertilizer company OCI Nitrogen.
Can’t wait for our next blog post?
Watch the Application of Advanced Analytics in the Fertilizer Industry webinar now to see these use case examples first hand.